Assessment of Toll-like receptor 2 gene polymorphisms in severe chronic rhinosinusitis.
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) is believed to reflect an inflammatory response of the sinonasal mucosa to bacteria and/or fungi. Staphylococcus aureus, a gram-positive organism, is frequently implicated. Toll-like receptor 2 (TLR2) is involved in innate immunity, recognizing gram-positive organisms via detection of bacterial lipopeptides. As a poor response to sinus surgery has been associated with reduced levels of TLR2 expression, and given the frequent recovery of S. aureus in this condition, we suspected that polymorphisms in TLR2 genes are implicated in this condition. OBJECTIVE: To investigate the association between single nucleotide polymorphisms (SNPs) in the TLR2 gene and CRS. METHODS: Two hundred six patients with severe CRS and 200 controls were recruited prospectively. A maximally informative set of SNPs in the gene encoding TLR2 were selected from the HapMap data set and genotyped. RESULTS: Eleven of 12 SNPs were successfully genotyped. No significant associations could be detected for the SNPs tested within the limitations of our study, which has the power to detect only those SNPs with a relative risk of 2.0 or greater. CONCLUSIONS: Our findings do not support a role for polymorphisms in the TLR2 gene in the pathogenesis of CRS. Nevertheless, other genetic variants within genes regulating innate immunity may be involved and will require further assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".